Real-Time and Off-Line Tools for Monitoring and Analysis of Power System Components

ABSTRACT

Various methods and systems are provided for impulse response monitoring in power systems. In one embodiment, a method includes obtaining raw power system data associated with a power system, cross-correlating the raw power system data with a synchronized pseudo-random sequence signal injected into the power system to determine a correlated impulse response and determining a condition of the power system based at least in part upon the correlated impulse response. In another embodiment, a system includes a plurality of signal injection systems and a data capture device coupled to a power system. A data analysis device cross-correlates raw power system data obtained by the data capture device with at least one synchronized pseudo-random sequence signal injected by a signal injection system and determines a condition of the power system based at least in part upon a frequency spectrum based upon a correlated impulse response.

BACKGROUND

Electric utilities operating a power grid take measurements of powersystem parameters such as voltage, current and phase angle informationat various points throughout their operating territories and apply themto mathematical models of the power system, its connectivity, and itsvarious components. Information derived from these models is then usedas a means of monitoring the power system and providing information foroperators and coordinators.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the present disclosure can be better understood withreference to the following drawings. The components in the drawings arenot necessarily to scale, emphasis instead being placed upon clearlyillustrating the principles of the present disclosure. Moreover, in thedrawings, like reference numerals designate corresponding partsthroughout the several views.

FIG. 1 is a drawing of a system for impulse response and frequencymonitoring in a power system according to various embodiments of thepresent disclosure.

FIG. 2 is a graphical representation illustrating an example ofsynchronized pseudo-random sequence (PRS) signal injection into thepower system of FIG. 1 according to various embodiments of the presentdisclosure.

FIG. 3 is graphical plots of an example of a PRS for injection into thepower system of FIG. 1 according to various embodiments of the presentdisclosure.

FIG. 4 is a graphical representation illustrating an example ofgenerating various uncorrelated PRS signals in the PRS generator of FIG.2 using a linear feedback shift register (LFSR) in accordance withvarious embodiments of the present disclosure.

FIG. 5 is a graphical representation illustrating an example of a signalconditioning interface of FIGS. 2 and 8 according to various embodimentsof the present disclosure.

FIG. 6 is graphical plots of an example of the PRS of FIG. 3 afterconditioning by the signal conditioning interface of FIG. 5 according tovarious embodiments of the present disclosure.

FIG. 7 is a graphical representation illustrating an example of acoupling capacitor voltage transformer (CCVT) used as a power systeminterface of FIGS. 2 and 8 according to various embodiments of thepresent disclosure.

FIG. 8 is a graphical representation illustrating an example ofsynchronized raw power system data capture from the power system of FIG.1 according to various embodiments of the present disclosure.

FIG. 9 is graphical plots of an example of raw power system datacaptured and correlated by the embodiment of FIG. 8 according to variousembodiments of the present disclosure.

FIG. 10 is graphical plots of an example of frequency spectrums of thecaptured and correlated raw power system data of FIG. 9 according tovarious embodiments of the present disclosure.

FIG. 11 is a graphical plot of an example of a least squares differenceof frequency spectrums of correlated impulse responses according tovarious embodiments of the present disclosure.

FIGS. 12 and 13 are graphical plots of examples of correlated impulseresponse frequency spectrums of a coupling capacitor voltage transformer(CCVT) of FIG. 7 operating with various capacitor conditions accordingto various embodiments of the present disclosure.

FIGS. 14-16 are graphical representations illustrating examples ofreal-time and off-line power system impulse and frequency responsemonitoring and analysis in accordance with various embodiments of thepresent disclosure

FIG. 17 is a flowchart illustrating examples of functionalityimplemented as portions of a data capture application and/or a dataanalysis application executed in one or more computing device(s) in thesynchronized data capture and/or analysis of FIG. 8 according to variousembodiments of the present disclosure.

FIG. 18 is a graphical representation of a networked environment forsynchronized PRS signal injection and/or raw power system data capturefor the power system of FIG. 1 according to various embodiments of thepresent disclosure.

FIG. 19 is a schematic block diagram that provides one exampleillustration of a computing device employed in the synchronized datacapture and/or analysis of FIG. 8 according to various embodiments ofthe present disclosure.

DETAILED DESCRIPTION

Disclosed herein are various embodiments of methods related to impulseresponse monitoring in power systems. Reference will now be made indetail to the description of the embodiments as illustrated in thedrawings, wherein like reference numbers indicate like parts throughoutthe several views.

Introducing a low level of electrical white noise to a power system cancause electrical elements of the system to resonate (or ring) at theircharacteristic frequencies. The resulting resonant response can beanalyzed to identify and monitor elements of the power system beingstimulated by the introduced signal. The power system elements caninclude, but are not limited to, coupling capacitor voltage transformers(CCVT), switched capacitor banks, tap-changing transformers, circuitbreakers, transmission lines, and other power system components as canbe appreciated. Using pattern recognition techniques, abnormal andfailing elements can be detected and identified before substantiallyaffecting the power system. In addition, changes in the configuration ofthe power system network may also be detected and identified. Suchdetection and identification may be carried out continuously and in realtime.

Referring to FIG. 1, shown is a graphical representation illustratingimpulse signal injection for monitoring a power system 103 in accordancewith various embodiments of the present disclosure. Independent of thepower flow through the power system 103, a pseudo-random sequence (PRS)signal is injected by system 106 into the power system 103 through apower system interface 109 a in a transmission path 112 (e.g., a HVtransmission line, bus, or other appropriate access point). The injectedsignal is a relatively low-level noise signal such as a PRS signal.After propagating through the power system 103, the resulting signal isobtained at another point 118 along the transmission path for analysis(e.g., through another power system interface 109 b by a data capturesystem 115). The PRS signal injection system 106 and the data capturesystem 115 are synchronized to facilitate analysis of the captured data.The analysis may be used to provide a real-time indication of the stateof the power system 103 as described in U.S. Pat. No. 7,848,897,entitled “Dynamic Real-Time Power System Monitoring” and issued on Dec.7, 2010, the entirety of which is hereby incorporated by reference.

The impulse response of the power system 103 can be determined by addinga random noise signal to the power system 103 through the power systeminterface 109 a and cross-correlating the captured data with theadditive random noise input signal. Pseudo-random discrete intervalbinary noise sequences can be used effectively as the noise inputsignal. Using cross-correlation and other techniques on the sampleddata, impulse and frequency response characteristics of the power systemand its components can be determined. For example, taking a Fouriertransform of the impulse response yields the frequency response of thesystem.

Referring next to FIG. 2, shown is an example of a system forsynchronized PRS signal injection 106 into the power system 103. In theembodiment of FIG. 2, a synchronous pulse generator 203 providesperiodic synchronization pulses based upon timing synchronization suchas, e.g., a GPS clock. The periodic synchronization pulses are used toinitiate the injection of a pseudo-random noise (PN) sequence into thepower system 103 through power system interface 109 a. Unlike a purewhite noise signal (i.e., a purely random signal) with energy spreadequally at all frequencies, the frequency response of a pseudo-randomdiscrete interval binary signal is a classic sin(x)/x shaped waveform.An example of a PRS signal is illustrated in FIG. 3. A portion of theraw data 303 of a PN9 signal at a bit rate clock frequency of 625 kHz isdepicted in the top trace. The resulting frequency spectrum 306 is shownin the bottom trace. The zero points on the frequency spectrum 306 occurat multiples of the bit rate clock frequency (i.e., n×625 kHz; n=1, 2, .. . ). Longer PN sequences have statistical characteristics that moreclosely approximate those of pure random noise waveforms and tend toproduce better quality calculated impulse and frequency responses.

Referring back to FIG. 2, the periodic synchronization pulses aresupplied for use by a PRS generator 206, which is configured to controlthe bit rate clocking and generation of the PRS. The PRS generator 206may provide PRS at one or more bit length(s), e.g., PN9, PN10, PN11, andPN12 sequences, for injection into the power system 103. In someembodiments, PRS bit lengths range from PN6 to PN17 where the PNsequence length is 2^(n)−1 for a selected PNn. In some implementations,the PN sequence length may be selectable.

The bit rate clock frequency may also be selectable from a range offrequencies. TABLE 1 provides examples of PRS durations at various bitlengths and bit rate clock frequencies that may be utilized. Higher bitrate clock frequencies tend to result in captured data that yields moredetail in the calculated impulse and frequency responses.

TABLE 1 Data sample rate at 10X 40 ns 80 ns 160 ns 320 ns 640 ns PRS bitclock period 400 ns 800 ns 1600 ns 3200 ns 6400 ns PRS bit clockfrequency 2.5 MHz 1.25 MHz 625 kHz 312.5 kHz 156.25 kHz Bit length PN9511 204.4 μs 408.8 μs 817.6 μs 1.64 ms 3.27 ms PN10 1023 408.8 μs 817.6μs 1.64 ms 3.27 ms 6.55 ms PN11 2047 817.6 μs 1.64 ms 3.27 ms 6.55 ms13.10 ms PN12 4095 1.64 ms 3.27 ms 6.55 ms 13.10 ms 26.21 ms

The duration of the PRS is the bit clock period times the bit length ofthe sequence. Cross-correlations of impulse responses are more effectivewhen the duration of the PRS is longer than the response of the powersystem 103 to an impulse. So the combination of bit rate and sequencelength should be chosen such that the PRS length in time exceeds thetotal time for a system's impulse response to die out. In the examplesof TABLE 1, the PRS durations range from a duration of 204.4microseconds to 26.21 milliseconds.

FIG. 4 illustrates an example of generating various uncorrelated PRSsignals in the PRS generator 206 using a linear feedback shift register(LFSR) in accordance with various embodiments of the present disclosure.In the embodiment of FIG. 4, an 11-bit LFSR is used to produce PN11sequences. A shift clock (e.g., the bit clock) is used to clock theshift register. An “exclusive OR operation” (XOR, odd parity) onselected output from the various stages of the shift register provides afeedback signal to the beginning stage of the shift register. Onlycertain selected outputs will produce maximum length LFSR sequences thatare pseudorandom. In the case of the 11-bit LFSR of FIG. 4, 88 unique PN11 sequences (PRS-1, PRS-2, PRS-3 . . . , and PRS-88) may be created.

Referring back to FIG. 2, the PRS is supplied to a binary drive control209 for injection of the PN sequence into the power system 103 at a lowvoltage level (e.g., less that about 100 V). In some embodiments, thedrive control 209 injects the PN signal at a low voltage level of about100 V peak-peak, about 50 V peak-peak, about 25 V peak-peak, or at otherlow voltages as can be appreciated. The binary drive control 209 maycontinuously inject a stream of PRS signals separated from each other bya synchronizing pulse. A description of a PRS generator 206 and a binarydrive control 209 of a PRS signal injection system 106 are provided inU.S. patent application Ser. No. 12/645,853, filed on Dec. 23, 2009 andentitled “Pseudorandom Binary Discrete Interal Noise Signal Generationand Injection on to the Electric Power Grid,” which is herebyincorporated by reference in its entirety. In one embodiment, thespecifications of a binary drive control 209 include a frequency rangefrom DC to about 1 MHz, a frequency response of less than +/−0.1 dB,distortion of less than 0.1%, a maximum voltage of about 140 Vrms (OC),a voltage gain of about 0 dB to about 40 dB, a variable DC offset ofabout 0V to +/−200V peak, continuous output power of about 75 Watts, andshort circuit protection.

A signal conditioning interface 212 a is provided between the powersystem interface 109 b and the output of the binary drive control 209 toprotect the PRS injection equipment from the power flow on the powersystem 103 (FIG. 1), as well as to avoid interference with power linecarriers and transfer trip systems. FIG. 5 illustrates one example,among others, of a signal conditioning interface 212, which includespassive elements to provide protection. An R-C filter may be used forsignal conditioning and passive series notch filters may be used toremove power line carrier signals. The embodiment of FIG. 5 depicts R-Csignal conditioning with a 50 ohm resistor and a 0.22 uf capacitor and athree stage notch filter tuned to block the appropriate carrierfrequency (or frequencies) such as, e.g., 179.5 kHz. Signals from thebinary drive control 209 are obtained at connection 403 and conditionedsignals are provided to the power system interface 109 b from connection406.

The use of a signal conditioning interface 212 a (FIG. 2) will alter thePRS signal waveform injected into the power system 103 (FIG. 1). Anexample of the effect of the R-C signal conditioning and L-C notchfilters tuned to 179.5 KHz is shown on a PRS signal is illustrated inFIG. 6. A portion of the raw data 503 of a conditioned PN10 signal at abit rate clock frequency of 312.5 kHz is depicted in the top trace. Theresulting frequency spectrum 506 is shown in the bottom trace. The zeropoints on the frequency spectrum 506 occur at multiples of the bit rateclock frequency (i.e., n×312.5 kHz; n=1, 2, 3 . . . ). The attenuationof the frequency spectrum 506 by the signal conditioning interface 212 acan be clearly seen around 179.5 KHz. Due to the deterministic nature ofthe effects on the PRS signal, the effects of a signal conditioninginterface 212 can be compensated for during analysis of the captureddata based upon simulated and/or measured characteristics of the signalconditioning interface 212.

Referring back to FIG. 2, the conditioned PRS signal from the signalconditioning interface 212 a is added to the power line carrier signalsof the power system 103 through the power system interface 109 a. Apower system interface 109 may be, for example, a coupling capacitorvoltage transformer (CCVT) that is coupled to the signal conditioninginterface 212 a and the transmission path 112 (FIG. 1) of the powersystem 103. FIG. 7 provides a graphical representation of an example ofa CCVT 603 and a diagram illustrating a connection of the CCVT 603 to ahigh voltage transmission line 609 of the power system 103. A stack ofcapacitors 606 in the CCVT 603 facilitates injection of the low voltagePRS signal into a high voltage bus or transmission line 609. Theconditioned PRS signal from the signal conditioning interface 212 a isprovided for injection across the drain coil of the CCVT 603 throughconnection 612.

Referring next to FIG. 8, shown is an example of a system forsynchronized data capture 115 from the power system 103. In theembodiment of FIG. 8, the response to the injected PRS signal isobtained through a power system interface 109 b. A power systeminterface 109 may be, for example, a coupling capacitor voltagetransformer (CCVT) 603 (FIG. 7) that is coupled to a signal conditioninginterface 212 b and the transmission path at point 118 (FIG. 1) of thepower system 103. A stack of capacitors 606 (FIG. 7) in the CCVT 603facilitates obtaining low voltage raw power system signal from the highvoltage transmission line 609 (FIG. 7) or bus (e.g., with ratings in thekV range). The signal conditioning interface 212 b receives the rawpower system data from across the drain coil of the CCVT 603 throughconnection 612 (FIG. 7). The signal conditioning interface 212 bprotects the data capture and analysis equipment from the power flow onthe power system 103 (FIG. 1), as well as to avoid interference withpower line carriers and transfer trip systems. In the example of asignal conditioning interface 212 illustrated in FIG. 5, signals aretaken from the CCVT 603 (FIG. 7) at connection 406 (FIG. 5) andconditioned signals are provided to an analog-to-digital (A/D) converter703 from connection 403 (FIG. 5). The use of a signal conditioninginterface 212 b will alter the PRS response waveform from the powersystem 103 (FIG. 1). However, due to the deterministic nature of theeffects on the PRS response, the effects of a signal conditioninginterface 212 can be compensated for during analysis of the captureddata based upon simulated and/or measured characteristics of the signalconditioning interface 212.

The PRS signal injection system 106 (FIG. 2) and data capture system 115(FIG. 8) are synchronized to facilitate analysis of the captured rawpower system data. As illustrated in FIG. 8, a synchronous pulsegenerator 706 provides periodic synchronization pulses based upon timingsynchronization such as, e.g., the GPS clock. The periodicsynchronization pulses are used by the A/D converter 703 tosynchronously sample the raw power system data from the signalconditioning interface 212 b. The sampled data may be buffered forcapture. For example, a Picoscope ADC-212 or other device may be used tosample and buffer the raw power system data.

In response to a trigger, the raw power system data is captured andstored by data capture device 709. For example, capture may be triggeredby the GPS clock (e.g., one pulse per second). Other triggers may beutilized as can be appreciated. In some implementations, a predefinedamount of raw power system data may be block captured in response to thetrigger. For example, the block size may be the PRS length×an oversamplerate. In other embodiments, the amount of captured raw power system datamay vary based upon the length of the PRS and/or other conditions of thepower system 103. For example, the block size may be adjusted based uponthe signal from the signal conditioning interface 212. In someembodiments, raw power system data corresponding to consecutive PRSsignals in a stream of PRS signals are captured to determine thecorrelated impulse response. In some cases, buffering by the A/Dconverter 703 may allow capture of data that was sampled beforetriggering. In some embodiments, the ND converter 703 may be included inthe data capture device 709.

Data capture device 709 may be, e.g., a hardware device, a data logger,a computing device such as, e.g., a laptop, workstation, smartphone,and/or from other computing device that is configured to execute a datacapture application, or other device as can be appreciated. The datacapture device 709 may also be configured to analyze the captured rawpower system data (e.g., by execution of a data analysis application) ora separate data analysis device 712 (e.g., another computing deviceconfigured to execute a data analysis application) may obtain thecaptured raw power system data for analysis. In some implementations,the PRS signal injection system 106 and/or data capture system 115 maybe adjusted based upon the captured and/or analyzed data to improve datacapture. In some embodiments, the captured data may be stored in a datastore for subsequent analysis.

To begin, the captured raw power system data is cross-correlated withone or more PRS. FIG. 9 shows an example of a captured data waveform803, which is the result of a PN11 signal with a bit clock period of 400ns, and a correlated impulse response waveform 806. The correlatedimpulse response waveform 806 is determined by cross-correlating thecaptured raw power system data 803 with the injected PN11 sequence. Insome implementations, the cross-correlation determination is calculatedin real-time. The correlated impulse response waveform 803 may be usedto determine a condition of the power system 103. For example, acondition of the power system 103 may be based at least in part uponundershoot, overshoot, ringing, delays, and/or other characteristics inthe correlated impulse response waveform 803. In one embodiment, thedelay 809 to the first spike in the correlated impulse response 803corresponds to the propagation time of an actual impulse through, e.g.,a transmission line. By evaluating changes in this delay 809, acondition such as, e.g., a change in transmission line length due tosagging may be determined. The condition of other components such as,but not limited to, CCVTs and carrier traps may also be determined usingthe correlated impulse response.

In response to the cross-correlation, a frequency spectrum of thecorrelated impulse response may then be determined. In some embodiments,the cross-correlation results may then be compared to a predefinedthreshold to determine if a correlation exists between the PRS and thecaptured data. FIG. 10 shows an example of a frequency spectrum 903 ofthe captured raw power system data 803 (FIG. 9) and a frequency spectrum906 of the correlated impulse response waveform 806 (FIG. 9). A Fouriertransform of the data 803 and 806 is used to determine each respectivefrequency spectrum 903 and 906. In some implementations, the frequencyspectrum is calculated in real-time. Frequency spectrum(s) of correlatedimpulse response(s) may be used to determine a condition of the powersystem 103 (FIG. 1) as will be described below. For example, thefrequency spectrum(s) and/or impulse response(s) may be used todetermine the configuration of the power system 103 or the condition ofa component of the power system 103. In some embodiments, a plurality ofsequential frequency spectrums and/or impulse responses may be displayedfor comparison and/or to illustrate trending of the impulse responsesover time. In some embodiments, the impulse responses and/or frequencyspectrums may be tiled on a display for viewing and analysis.Coordinated resizing of the display area of the tiles allows for easycomparison of displayed information.

In addition, least squares analysis of correlated impulse responsewaveforms and/or frequency spectrums may also be used to determinecondition of the power system 103. For example, the least squaresdifference 1003 between the two most recent impulse response waveforms(and/or frequency spectrums) may be calculated as illustrated in FIG.11. Excursions in the least squares difference 1003 indicate a change inthe power system 103 while constant values illustrate repeatability ofthe impulse responses (and/or frequency spectrums). In some embodiments,a plurality of sequential least squares differences 1003 may bedisplayed to illustrate trending of the impulse responses (and/orfrequency spectrums) over time.

Referring next to FIGS. 12 and 13, shown are frequency spectrums ofcorrelated impulse responses for a CCVT, such as the example illustratedin FIG. 7. The CCVT included 90 capacitor elements. The frequencyspectrum 1103, presented at the bottom of FIG. 12, corresponds to thecorrelated impulse response of a PRS (a PN10 sequence with a 160 ns bitclock period) injected into a system including a CCVT without anyshorted capacitors. The frequency spectrum 1106, presented at the top ofFIG. 12, corresponds to the correlated impulse response of the PRS withone of the CCVT capacitors shorted. As can be seen in FIG. 12, thesingle shorted capacitor produces a detectable variation between thefrequency spectrums 1103 and 1106.

The condition of the CCVT may be determined based upon characteristicfrequencies and/or the impulse response associated with the CCVT. Byusing a range of frequencies 1109 (or sub-ranges of frequencies) as thecharacteristic frequencies, a pattern recognition algorithm or neuralnetwork may be used to determine the condition of the CCVT. For example,changes in the distribution of magnitudes within the characteristicfrequency range 1109 may be associated with a condition of the CCVT bypattern recognition. In other implementations, a neural network may betrained to provide an indication of the CCVT condition based uponlearned patterns within the frequency range 1109. Training data may beprovided based upon measured data or from simulation results. In someembodiments, multiple characteristic frequency components (or frequencyranges) may be recognized a characterizing a component within the powersystem 103, and may be used to determine a condition (e.g., the presenceof a fault) of the component.

The frequency spectrums of FIG. 13 further illustrate the variations inthe frequency response produced by shorting of various combinations ofcapacitors within the CCVT. The frequency spectrum 1203, presented atthe top left of FIG. 13, corresponds to the correlated impulse responseof another PRS (a PN9 sequence with a 400 ns bit clock period) injectedinto the system including a CCVT without any shorted capacitors. Thefrequency spectrum 1206, presented at the top right of FIG. 13,illustrates the impact on the frequency response to shorting onecapacitive element. The frequency spectrums 1209 and 1212, presented atthe bottom left and bottom right of FIG. 13, illustrates the impact onthe frequency response to shorting several capacitive elements and mostof the capacitive elements, respectively. The different patterns in FIG.13 allow for classification and identification based upon the frequencyresponse and/or the correlated impulse response of the CCVT.

Other components of the power system 103 (FIG. 1) also havecharacteristic frequencies that may be used to determine the conditionof the power system 103 and/or one or more component(s) included in thepower system. For example, the condition of capacitor banks,transformers, or other components may be determined using patternrecognition and/or neural network evaluation of the frequency spectrumof the correlated impulse response. In addition, power system 103conditions including, but not limited to, circuit breaker and/ortransmission line conditions may be identified based upon the frequencyresponse and/or the correlated impulse response of the power system 103.For example, as circuit breakers are opened or closed the impulseresponse of the power system 103 will change, and thus may be used todetermine the characteristics of the power system 103 and/or itscomponents. The frequency spectrum characteristics associated withvarious components within the power system 103 may be determined throughimpulse response measurements and/or simulation of the component(s)and/or power system 103.

In some embodiments, multiple PRS are injected from different locationswithin the power system 103. The corresponding impulse responses maythen be captured, cross-correlated, and used to determine thecondition(s) of the power system 103. In some cases, the impulseresponses of two or more PRS may be simultaneously captured by a datacapture device 709 (FIG. 8) in a single set of captured data. If the PRScorresponding to the simultaneously injected PRS signals areuncorrelated, the captured raw power system data can be cross-correlatedwith each of the uncorrelated PRS to determine the impulse response andfrequency spectrums associated with each PRS. If multiple uncorrelatedPRS are injected at the same time from different locations, the datacapture device 709 can be triggered in synchronization with the firstPRS signal. The GPS clock triggers both injection and capturesimultaneously. The impulse response corresponding to the different PRSmay be analyzed from the same set of captured raw power system databased upon cross-correlation with each of the uncorrelated PRS. Forexample, there are 88 unique PN 11 sequences. Therefore, 88 different PN11 sequences may be injected at 88 different locations around the powersystem. By cross-correlating each sequence with the raw power systemdata captured at a single point on the power system, the impulse andfrequency response can be calculated between the capture point and eachof the 88 different injection points. The stored data may includeinformation identifying the corresponding PRS.

In addition, the calculated impulse response corresponding to a singlePRS may be captured in a plurality of locations within the power system103. The frequency spectrums corresponding to the calculated impulseresponse may be used to determine the conditions of various componentsdistributed within the power system 103 as described above. Similarly, aplurality of uncorrelated PRS may be injected at various points in thepower system 103. Raw power system data may be captured at the same ordifferent points and cross-correlated with the uncorrelated PRS todetermine one or more condition(s) of the power system 103.

Raw power system data and/or calculated impulse response data may bestored in a data store for subsequent analysis. In addition, powersystem conditions may be associated with the stored data to identifyconditions in the power system 103 based upon pattern recognition orother methods. In some implementations, captured power system data maybe used to provide real-time indications of power system condition(s)and/or control inputs for power system operation. Stored data may alsobe used for subsequent analysis and identification of power systemcondition(s).

Referring to FIG. 14, shown is an example of real-time power systemimpulse and frequency response monitoring in accordance with variousembodiments of the present disclosure. In the example of FIG. 14, a PRSis injected at a first location 1403 in a power system 103 using a PRSsignal injection system 106 (FIG. 1). Raw power system data 1409 iscaptured at a second location 1406 in the power system 103 using thedata capture system 115 (FIG. 1). The PRS signal injection system 106and the data capture system 115 are synchronized to facilitate analysisof the captured data 1409. In some implementations, the raw power systemdata 1409 is stored in a data store and/or memory for subsequentoff-line analysis as will be discussed. As can be understood, real-timemonitoring and analysis may be applied to the captured raw power systemdata associated with other injection/capture locations within the powersystem 103.

The captured raw power system data 1409 may be further processed forreal-time monitoring. For example, a Fourier transform of the captureddata 1409 can provide frequencies 1412 on the power system 103 atlocation 1406. The captured data 1409 may also be cross-correlated withthe PRS injected at location 1403 to provide the impulse response 1415between locations 1403 and 1406. A Fourier transform of the impulseresponse 1415 can provide a frequency response 1418 of the power system103 between locations 1403 and 1406. A least squared sample difference1421 between the current and a previous impulse response 1415 and/orfrequency response 1418 may also be calculated. Some or all of thedetermined power system information (e.g., the captured raw power systemdata 1409, the power system frequencies 1412, the impulse response 1415,the frequency response 1418, and/or the least squared sample 1421) maybe used to determine a condition of the power system 103.

Graphical representations of the determined power system information maybe generated and provided for rendering on a display device. FIG. 14illustrates an example of a window layout 1424 for rendering on thedisplay device. The window layout 1424 provides for monitoring andanalysis of the current condition of the power system 103 andindications of changes in the power system 103 based upon the determinedpower system information 1409-1421 corresponding to locations 1403 and1406. Screen shot 1427 depicts an example of a rendered window includingthe power system information 1409-1421 using layout 1424.

Referring now to FIGS. 15 and 16, shown are examples of off-line powersystem impulse and frequency response analysis in accordance withvarious embodiments of the present disclosure. For off-line analysis,captured raw power system data 1409 is obtained from a data store and/ormemory. In the examples of FIGS. 15 and 16, reference is made tocaptured data 1409 injected at location 1403 and captured at location1406 of power system 103 as illustrated in FIG. 14. As can beunderstood, off-line analysis may be applied to the captured raw powersystem data associated with other injection/capture locations within thepower system 103.

The captured raw power system data 1409 is then processed for off-lineanalysis. As in FIG. 14, a Fourier transform of the captured data 1409can provide frequencies 1412 on the power system 103 at location 1406.The captured data 1409 may also be cross-correlated with the PRSinjected at location 1403 to provide the impulse response 1415 betweenlocations 1403 and 1406. A Fourier transform of the impulse response1415 can provide a frequency response 1418 of the power system 103between locations 1403 and 1406. A least squared sample difference 1421between the current and a previous impulse response 1415 and/orfrequency response 1418 may also be calculated. Some or all of thedetermined power system information (e.g., the captured raw power systemdata 1409, the power system frequencies 1412, the impulse response 1415,the frequency response 1418, and the least squared sample 1421) may beused to determine a condition of the power system 103. In someembodiments, power system information for a plurality ofinjection/capture times may be determined for comparison and analysis todetermine conditions and/or changes in the power system 103.

Graphical representations of the determined power system information maybe generated and provided for rendering on a display device. FIG. 15illustrates an example of two window layouts 1524 of power systeminformation 1409-1418 for rendering on one or more display device(s).One layout 1524 a provides for analysis of the condition of the powersystem 103 and indications of changes in the power system 103 based uponthe determined power system information 1409 and 1412 corresponding tolocation 1406 at one injection/capture time. The other layout 1524 bprovides for analysis of the condition of the power system 103 andindications of changes in the power system 103 based upon the determinedpower system information 1415 and 1418 corresponding to locations 1403and 1406 at the same injection/capture time. Screen shots 1527 a and1527 b depict examples of rendered windows including the power systeminformation 1409-1412 and 1415-1418 using layouts 1524 a and 1524 b,respectively.

FIG. 16 illustrates an example of two window layouts 1624 of powersystem information 1415-1418 for rendering on one or more displaydevice(s). One layout 1624 a provides for side-by side analysis of thecondition of the power system 103 and indications of changes in thepower system 103 based upon the impulse response 1415 a and 1415 bcorresponding to locations 1403 and 1406 at different injection/capturetimes. The other layout 1624 b provides for side-by side analysis of thecondition of the power system 103 and indications of changes in thepower system 103 based upon the frequency response 1418 corresponding tolocations 1403 and 1406 at different injection/capture times. Screenshots 1627 a and 1627 b depict examples of rendered windows includingthe impulse response 1415 and frequency response 1418 using layouts 1624a and 1624 b, respectively. In some embodiments, operations such aszooming or changing displayed ranges may be coordinated between windowframes including the same power system information (e.g., impulseresponse 1415 a and 1415 b or frequency response 1418 a-1418 d) suchthat a modification to one frame is simultaneously carried out in allother frames including the same information. For example, impulseresponses 1415 a and 1415 b can be displayed with the same scaling. Ifthe displayed range of impulse response 1415 a is adjusted, then thedisplayed range of impulse response 1415 b simultaneously changes to thesame scaling.

Referring next to FIG. 17, shown is a flowchart illustrating an exampleof functionality implemented as portions of the data capture and/or dataanalysis according to various embodiments of the present disclosure. Itis understood that the flowchart of FIG. 17 provides merely an exampleof the many different types of functional arrangements that may beemployed to implement the operation of the portion of the data captureand/or data analysis as described herein. As an alternative, theflowchart of FIG. 17 may be viewed as depicting examples of steps of amethod implemented in the data capture device 709 and/or data analysisdevice 712 (FIG. 8) according to one or more embodiments.

In the implementation of FIG. 17, raw power system data is obtained froma power system 103 (FIG. 1) in block 1703. In block 1706, the raw powersystem data is cross-correlated with a synchronized pseudo-randomsequence (PRS), which was injected into the power system 103. Thesynchronized PRS may be one of a plurality of uncorrelated PRS that havebeen injected into the power system 103. In some embodiments, the rawpower system data is cross-correlated with each of the uncorrelated PRS.A frequency spectrum is determined in block 1709 based upon thecross-correlated impulse response. The determination of the frequencyspectrum may be in response to the cross-correlation meeting somepredefined criteria or threshold condition. In some implementations, aplurality of frequency spectrums are determined in response to thecross-correlations, where each of the frequency spectrums is based upona cross-correlated impulse response corresponding to one of theuncorrelated PRS.

A condition of the power system 103 is determined in block 1712 based atleast in part upon the one or more frequency spectrum(s), impulseresponse data, and/or other system characteristics. The condition of thepower system 103 may include the configuration of the power system 103and/or a condition of a component included in the power system 103. Forexample, the component may be a coupling capacitor voltage transformer(CCVT), transformer, circuit breaker, transmission line, carrier trap,or other component included in a power transmission system as can beappreciated. The condition may correspond to a current operatingcondition or an existing fault condition. For example, the condition maybe a change in a transformer winding such as, but not limited to,changes in tap position, arcing or shorted turns, and/or shifting of thewinding or core. The condition of the power system 103 may be determinedbased upon changes in characteristic frequencies and/or the correlatedimpulse response associated with at least a portion of the power system103 and/or a component of the power system 103 using pattern recognitionalgorithms, neural networks, or other rule based identification methodsas can be appreciated. The characteristic frequencies can includefrequency components and/or frequency ranges of the frequencyspectrum(s).

Referring next to FIG. 18, shown is a networked environment forsynchronized PRS signal injection system 106 and/or data capture system115. The synchronized PRS signal injection system(s) 106 and/or datacapture system(s) 115 are located throughout the power system 103 (FIG.1). The synchronized PRS signal injection system 106 and/or data capturesystem 115 may be in communication with one or more central monitoringsystem(s) 1803 through a network 1806. The network 1806 includes, forexample, the Internet, intranets, extranets, wide area networks (WANs),local area networks (LANs), wired networks, wireless networks, powerline carrier networks, or other suitable networks, etc., or anycombination of two or more such networks. The synchronized PRS signalinjection system 106 and/or data capture system 115 may operateindependently or their operation may be coordinated by the centralmonitoring system(s) 1803. For example, the central monitoring system(s)1803 may coordinate PRS injection into the power system 103 tofacilitate capture of a single set of raw power system data foranalysis. In some implementations, the raw power system data may becaptured by a data capture device 709 and analyzed locally by a dataanalysis device 712 or remotely by the central monitoring system(s)1803. The central monitoring system(s) 1803 may also obtain captured rawpower system data and/or impulse response frequency spectrums frommultiple locations for a coordinated analysis of the power systemcondition. In addition, the central monitoring system(s) 1803 may allowselection of PRS parameters such as PN sequence length, PN sequencepattern, PRS magnitude, and/or bit clock period or frequency. Graphicalrepresentations and/or interactive interfaces may be provided directlyand/or through the network 1806 for rendering by display device(s) 1809.Graphical representations may be displayed from captured raw powersystem data before storing or may be displayed from previously storeddata. For example, impulse response data and/or frequency spectrum datafor one or more correlated impulse response(s) may be displayed duringreal time data capture or during off-line operation. In someembodiments, an interactive interface may allow for configuration of thePRS signal injection system 106 and/or data capture system 115 such as,but not limited to, selection of PNS length, injection voltage,timebase, number of samples, oversampling, captured data file location,buffer size, etc.

The central monitoring system(s) 1803 may include, but are not limitedto, Energy Management Systems (EMS), Supervisory Control and DataAcquisition (SCADA) systems, or other monitoring systems as can beappreciated. Analysis of the impulse response frequency spectrums may beused to provide a real-time indication of the state of the power system103 through the central monitoring system(s) 1803 as described in U.S.Pat. No. 7,848,897, entitled “Dynamic Real-Time Power System Monitoring”and issued on Dec. 7, 2010, the entirety of which is hereby incorporatedby reference. The central monitoring system(s) 1803 may generate one ormore graphical representation(s) and/or window(s) for rendering ondisplay device(s) 1809.

The graphical window can provide control center users (i.e., operators,engineers, planners and coordinators) with a visual depiction of thecondition of the power system 103. For example, a graphicalrepresentation of the power system 103 may include a color coded displaycorresponding to the condition of the power system 103 and/or componentsin the power system 103. These visual depictions may be geographicallybased, including the spatial orientation of the actual source locationscollecting the impulse data from substations, generating plants and tielines throughout the grid of the power system 103.

Overall impulse response parameters associated with the power system 103such as, but not limited to, connectiveness and responsiveness may alsobe determined based upon the determined condition of the power system103. In some embodiments, graphical representations of the impulseresponse, frequency spectrum, and/or least squares differences, asillustrated in FIGS. 9-11, may be generated for rendering on a displaydevice 1809. The central monitoring system(s) 1803 may also use thecondition of the power system 103 to automatically adjust the operationof the power system 103.

With reference to FIG. 19, shown is a schematic block diagram of acomputing device 1900 according to various embodiments of the presentdisclosure. The computing device 1900 includes at least one processorcircuit, for example, having a processor 1903 and a memory 1906, both ofwhich are coupled to a local interface 1909. To this end, the computingdevice 1900 may comprise, for example, at least one server computer orlike device. The local interface 1909 may comprise, for example, a databus with an accompanying address/control bus or other bus structure ascan be appreciated.

Stored in the memory 1906 are both data and several components that areexecutable by the processor 1903. In particular, stored in the memory1906 and executable by the processor 1903 are a data capture application1915, a data analysis application 1918, and/or other applications 1921.Also stored in the memory 1906 may be a data store 1912 and other data.In addition, an operating system may be stored in the memory 1906 andexecutable by the processor 1903.

It is understood that there may be other applications that are stored inthe memory 1906 and are executable by the processor 1903 as can beappreciated. Where any component discussed herein is implemented in theform of software, any one of a number of programming languages may beemployed such as, for example, C, C++, C#, Objective C, Java®,JavaScript®, Pen, PHP, Visual Basic®, Python®, Ruby, Delphi®, Flash®, orother programming languages.

A number of software components are stored in the memory 1906 and areexecutable by the processor 1903. In this respect, the term “executable”means a program file that is in a form that can ultimately be run by theprocessor 1903. Examples of executable programs may be, for example, acompiled program that can be translated into machine code in a formatthat can be loaded into a random access portion of the memory 1906 andrun by the processor 1903, source code that may be expressed in properformat such as object code that is capable of being loaded into a randomaccess portion of the memory 1906 and executed by the processor 1903, orsource code that may be interpreted by another executable program togenerate instructions in a random access portion of the memory 1906 tobe executed by the processor 1903, etc. An executable program may bestored in any portion or component of the memory 1906 including, forexample, random access memory (RAM), read-only memory (ROM), hard drive,solid-state drive, USB flash drive, memory card, optical disc such ascompact disc (CD) or digital versatile disc (DVD), floppy disk, magnetictape, or other memory components.

The memory 1906 is defined herein as including both volatile andnonvolatile memory and data storage components. Volatile components arethose that do not retain data values upon loss of power. Nonvolatilecomponents are those that retain data upon a loss of power. Thus, thememory 1906 may comprise, for example, random access memory (RAM),read-only memory (ROM), hard disk drives, solid-state drives, USB flashdrives, memory cards accessed via a memory card reader, floppy disksaccessed via an associated floppy disk drive, optical discs accessed viaan optical disc drive, magnetic tapes accessed via an appropriate tapedrive, and/or other memory components, or a combination of any two ormore of these memory components. In addition, the RAM may comprise, forexample, static random access memory (SRAM), dynamic random accessmemory (DRAM), or magnetic random access memory (MRAM) and other suchdevices. The ROM may comprise, for example, a programmable read-onlymemory (PROM), an erasable programmable read-only memory (EPROM), anelectrically erasable programmable read-only memory (EEPROM), or otherlike memory device.

Also, the processor 1903 may represent multiple processors 1903 and thememory 1906 may represent multiple memories 1906 that operate inparallel processing circuits, respectively. In such a case, the localinterface 1909 may be an appropriate network 1806 (FIG. 18) thatfacilitates communication between any two of the multiple processors1903, between any processor 1903 and any of the memories 1906, orbetween any two of the memories 1906, etc. The local interface 1909 maycomprise additional systems designed to coordinate this communication,including, for example, performing load balancing. The processor 1903may be of electrical or of some other available construction.

Although the data capture application 1915, the data analysisapplication 1918, application(s) 1921, and other various systemsdescribed herein may be embodied in software or code executed by generalpurpose hardware as discussed above, as an alternative the same may alsobe embodied in dedicated hardware or a combination of software/generalpurpose hardware and dedicated hardware. If embodied in dedicatedhardware, each can be implemented as a circuit or state machine thatemploys any one of or a combination of a number of technologies. Thesetechnologies may include, but are not limited to, discrete logiccircuits having logic gates for implementing various logic functionsupon an application of one or more data signals, application specificintegrated circuits having appropriate logic gates, or other components,etc. Such technologies are generally well known by those skilled in theart and, consequently, are not described in detail herein.

The flowchart of FIG. 17 shows the functionality and operation of animplementation of portions of the data capture application 1915 and/orthe data analysis application 1918. If embodied in software, each blockmay represent a module, segment, or portion of code that comprisesprogram instructions to implement the specified logical function(s). Theprogram instructions may be embodied in the form of source code thatcomprises human-readable statements written in a programming language ormachine code that comprises numerical instructions recognizable by asuitable execution system such as a processor 1903 in a computer systemor other system. The machine code may be converted from the source code,etc. If embodied in hardware, each block may represent a circuit or anumber of interconnected circuits to implement the specified logicalfunction(s).

Although the flowchart of FIG. 17 shows a specific order of execution,it is understood that the order of execution may differ from that whichis depicted. For example, the order of execution of two or more blocksmay be scrambled relative to the order shown. Also, two or more blocksshown in succession in FIG. 17 may be executed concurrently or withpartial concurrence. Further, in some embodiments, one or more of theblocks shown in FIG. 17 may be skipped or omitted. In addition, anynumber of counters, state variables, warning semaphores, or messagesmight be added to the logical flow described herein, for purposes ofenhanced utility, accounting, performance measurement, or providingtroubleshooting aids, etc. It is understood that all such variations arewithin the scope of the present disclosure.

Also, any logic or application described herein, including the datacapture application 1915, the data analysis application 1918, and/orapplication(s) 1921, that comprises software or code can be embodied inany non-transitory computer-readable medium for use by or in connectionwith an instruction execution system such as, for example, a processor1903 in a computer system or other system. In this sense, the logic maycomprise, for example, statements including instructions anddeclarations that can be fetched from the computer-readable medium andexecuted by the instruction execution system. In the context of thepresent disclosure, a “computer-readable medium” can be any medium thatcan contain, store, or maintain the logic or application describedherein for use by or in connection with the instruction executionsystem. The computer-readable medium can comprise any one of manyphysical media such as, for example, magnetic, optical, or semiconductormedia. More specific examples of a suitable computer-readable mediumwould include, but are not limited to, magnetic tapes, magnetic floppydiskettes, magnetic hard drives, memory cards, solid-state drives, USBflash drives, or optical discs. Also, the computer-readable medium maybe a random access memory (RAM) including, for example, static randomaccess memory (SRAM) and dynamic random access memory (DRAM), ormagnetic random access memory (MRAM). In addition, the computer-readablemedium may be a read-only memory (ROM), a programmable read-only memory(PROM), an erasable programmable read-only memory (EPROM), anelectrically erasable programmable read-only memory (EEPROM), or othertype of memory device.

It should be emphasized that the above-described embodiments of thepresent disclosure are merely possible examples of implementations setforth for a clear understanding of the principles of the disclosure.Many variations and modifications may be made to the above-describedembodiment(s) without departing substantially from the spirit andprinciples of the disclosure. All such modifications and variations areintended to be included herein within the scope of this disclosure andprotected by the following claims.

It should be noted that ratios, concentrations, amounts, and othernumerical data may be expressed herein in a range format. It is to beunderstood that such a range format is used for convenience and brevity,and thus, should be interpreted in a flexible manner to include not onlythe numerical values explicitly recited as the limits of the range, butalso to include all the individual numerical values or sub-rangesencompassed within that range as if each numerical value and sub-rangeis explicitly recited. To illustrate, a concentration range of “about0.1% to about 5%” should be interpreted to include not only theexplicitly recited concentration of about 0.1 wt % to about 5 wt %, butalso include individual concentrations (e.g., 1%, 2%, 3%, and 4%) andthe sub-ranges (e.g., 0.5%, 1.1%, 2.2%, 3.3%, and 4.4%) within theindicated range. The term “about” can include traditional roundingaccording to significant figures of numerical values. In addition, thephrase “about ‘x’ to ‘y’” includes “about ‘x’ to about ‘y’”.

1. A method, comprising: obtaining, in at least one computing device,raw power system data associated with a power system; cross-correlating,in at least one computing device, the raw power system data with asynchronized pseudo-random sequence signal injected into the powersystem to determine a correlated impulse response; and determining, inat least one computing device, a condition of the power system based atleast in part upon the correlated impulse response.
 2. The method ofclaim 1, further comprising: determining a frequency spectrum inresponse to the cross-correlation, the frequency spectrum based upon thecorrelated impulse response; and determining a condition of the powersystem based at least in part upon the correlated impulse response. 3.The method of claim 1, wherein determining the condition of the powersystem comprises determining a condition of a component included in thepower system.
 4. The method of claim 3, wherein the component includedin the power system is a coupling capacitor voltage transformer (CCVT).5. The method of claim 4, wherein the condition is a shorted capacitorin the CCVT.
 6. The method of claim 3, wherein the condition of thecomponent is a change in a transformer winding.
 7. The method of claim3, wherein the condition of the component is a change in transmissionline length due to sagging.
 8. The method of claim 1, wherein thecondition of the power system is based at least upon changes in thefrequency spectrum at characteristic frequencies associated with atleast a portion of the power system.
 9. The method of claim 8, whereinthe characteristic frequencies are associated with a component includedin the power system.
 10. The method of claim 8, wherein thecharacteristic frequencies are a range of frequencies of the frequencyspectrum.
 11. The method of claim 1, further comprisingcross-correlating the raw power system data with at least one additionalsynchronized pseudo-random sequence signal injected into the powersystem.
 12. The method of claim 11, further comprising determining atleast one additional frequency spectrum in response to thecross-correlation with the at least one additional synchronizedpseudo-random sequence signal, the at least one additional frequencyspectrum based upon the correlated impulse response corresponding to theat least one additional synchronized pseudo-random sequence signal. 13.A system, comprising: a plurality of signal injection systems coupled toa power system at a plurality of points, each signal injection systemconfigured to inject a different one of a plurality of uncorrelatedsynchronized pseudo-random sequence signals into the power system; adata capture device coupled to the power system, the data capture deviceconfigured to obtain raw power system data from the power system; and adata analysis device configured to: cross-correlate the raw power systemdata with at least one of the plurality of uncorrelated synchronizedpseudo-random sequence signals; determine a frequency spectrumassociated with the at least one uncorrelated synchronized pseudo-randomsequence signal, the frequency spectrum based upon a correlated impulseresponse corresponding to the at least one uncorrelated synchronizedpseudo-random sequence signal; and determine a condition of the powersystem based at least in part upon the frequency spectrum.
 14. Thesystem of claim 13, wherein the data analysis device is configured tocross-correlate the raw power system data with each of the plurality ofuncorrelated synchronized pseudo-random sequence signals.
 15. The systemof claim 14, wherein the frequency spectrum is determined in response toa comparison of the correlated impulse response corresponding to the atleast one uncorrelated synchronized pseudo-random sequence signal with apredefined threshold.
 16. The system of claim 13, wherein the dataanalysis device is further configured to: determine a frequency spectrumassociated with a second of the plurality of uncorrelated synchronizedpseudo-random sequence signals, the frequency spectrum based upon thecorrelated impulse response corresponding to the second uncorrelatedsynchronized pseudo-random sequence signal; and determine a condition ofthe power system based at least in part upon the first and secondfrequency spectrums.
 17. The system of claim 13, wherein the dataanalysis device is further configured to: determine a frequency spectrumassociated with a second of the plurality of uncorrelated synchronizedpseudo-random sequence signals, the frequency spectrum based upon thecorrelated impulse response corresponding to the second uncorrelatedsynchronized pseudo-random sequence signal; and determine anothercondition of the power system based at least in part upon the secondfrequency spectrums.
 18. The system of claim 13, wherein thepseudo-random sequence signals are pseudo-random sequence signals havingthe same bit length.
 19. The system of claim 13, wherein thepseudo-random sequence signals are simultaneously injected into thepower system.
 20. The system of claim 13, wherein the signal injectionsystems are coupled to the power system by power system interfaces. 21.The system of claim 13, wherein the data capture device and the dataanalysis device are the same device.
 22. A non-transitorycomputer-readable medium embodying a program executable in a computingdevice, the program comprising: code that obtains raw power system dataassociated with a power system; code that cross-correlates the raw powersystem data with a synchronized pseudo-random sequence signal injectedinto the power system to determine a correlated impulse response; andcode that determines a condition of the power system based at least inpart upon the correlated impulse response.